Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. However, an open problem remains in designing fitness functions that effectively capture the desired aesthetics of abstract outputs. In this work, we explore two methods for evaluating the aesthetics of a population using Vision-Language Models (VLMs). The first method uses CLIP-IQA to predict an aesthetic score for each design. The second method instead pits candidates against each other, with winners determined by a VLM using a custom prompt specified by the user. The outcomes of these pairwise comparisons are then used to estimate a population ranking via the Glicko rating system. We present these methods in the context of a case study using a custom generative system and compare the resulting rankings with an artist's aesthetic ranking and those produced by other aesthetic evaluation techniques. Additionally, we document the artist's experience using these approaches to evolve designs, critically analysing the strengths and weaknesses of both methods.

翻译：演化系统在创意领域展现出显著成果，近期已应用于生成式字体设计、平面设计及音乐创作。然而，如何构建能有效捕捉抽象输出中理想美感的适应度函数仍是一个开放性问题。本研究探索了两种利用视觉语言模型(VLM)评估种群美学价值的方法：第一种方法采用CLIP-IQA为每个设计预测美学得分；第二种方法则通过用户指定的自定义提示词，利用VLM对候选设计进行两两对抗判定，再基于Glicko评级系统根据胜负结果估算种群排名。我们以自定义生成系统为案例研究呈现上述方法，并将其排名结果与艺术家的审美排序及其他美学评估技术得出的排名进行对比。此外，本文还记录了艺术家运用这些方法进行设计演化的实践经验，批判性分析了两种方法的优劣。